{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "from pprint import pprint\n",
    "from lxml import etree\n",
    "import time\n",
    "import re\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # 爬取数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 爬1-100页"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "正在爬取第1页的数据\n",
      "正在爬取第2页的数据\n",
      "正在爬取第3页的数据\n",
      "正在爬取第4页的数据\n",
      "正在爬取第5页的数据\n",
      "正在爬取第6页的数据\n",
      "正在爬取第7页的数据\n",
      "正在爬取第8页的数据\n",
      "正在爬取第9页的数据\n",
      "正在爬取第10页的数据\n",
      "正在爬取第11页的数据\n",
      "当页数据写入失败\n",
      "正在爬取第12页的数据\n",
      "正在爬取第13页的数据\n",
      "正在爬取第14页的数据\n",
      "正在爬取第15页的数据\n",
      "正在爬取第16页的数据\n",
      "正在爬取第17页的数据\n",
      "正在爬取第18页的数据\n",
      "正在爬取第19页的数据\n",
      "正在爬取第20页的数据\n",
      "正在爬取第21页的数据\n",
      "正在爬取第22页的数据\n",
      "正在爬取第23页的数据\n",
      "正在爬取第24页的数据\n",
      "正在爬取第25页的数据\n",
      "正在爬取第26页的数据\n",
      "正在爬取第27页的数据\n",
      "正在爬取第28页的数据\n",
      "正在爬取第29页的数据\n",
      "正在爬取第30页的数据\n",
      "正在爬取第31页的数据\n",
      "正在爬取第32页的数据\n",
      "正在爬取第33页的数据\n",
      "正在爬取第34页的数据\n",
      "正在爬取第35页的数据\n",
      "正在爬取第36页的数据\n",
      "正在爬取第37页的数据\n",
      "当页数据写入失败\n",
      "正在爬取第38页的数据\n",
      "正在爬取第39页的数据\n",
      "当页数据写入失败\n",
      "正在爬取第40页的数据\n",
      "正在爬取第41页的数据\n",
      "正在爬取第42页的数据\n",
      "正在爬取第43页的数据\n",
      "正在爬取第44页的数据\n",
      "正在爬取第45页的数据\n",
      "正在爬取第46页的数据\n",
      "正在爬取第47页的数据\n",
      "正在爬取第48页的数据\n",
      "正在爬取第49页的数据\n",
      "正在爬取第50页的数据\n",
      "正在爬取第51页的数据\n",
      "正在爬取第52页的数据\n",
      "正在爬取第53页的数据\n",
      "正在爬取第54页的数据\n",
      "正在爬取第55页的数据\n",
      "正在爬取第56页的数据\n",
      "正在爬取第57页的数据\n",
      "当页数据写入失败\n",
      "正在爬取第58页的数据\n",
      "正在爬取第59页的数据\n",
      "正在爬取第60页的数据\n",
      "正在爬取第61页的数据\n",
      "正在爬取第62页的数据\n",
      "正在爬取第63页的数据\n",
      "正在爬取第64页的数据\n",
      "正在爬取第65页的数据\n",
      "正在爬取第66页的数据\n",
      "正在爬取第67页的数据\n",
      "正在爬取第68页的数据\n",
      "正在爬取第69页的数据\n",
      "正在爬取第70页的数据\n",
      "正在爬取第71页的数据\n",
      "正在爬取第72页的数据\n",
      "正在爬取第73页的数据\n",
      "正在爬取第74页的数据\n",
      "正在爬取第75页的数据\n",
      "正在爬取第76页的数据\n",
      "正在爬取第77页的数据\n",
      "正在爬取第78页的数据\n",
      "正在爬取第79页的数据\n",
      "正在爬取第80页的数据\n",
      "正在爬取第81页的数据\n",
      "正在爬取第82页的数据\n",
      "正在爬取第83页的数据\n",
      "正在爬取第84页的数据\n",
      "正在爬取第85页的数据\n",
      "正在爬取第86页的数据\n",
      "正在爬取第87页的数据\n",
      "正在爬取第88页的数据\n",
      "正在爬取第89页的数据\n",
      "正在爬取第90页的数据\n",
      "正在爬取第91页的数据\n",
      "正在爬取第92页的数据\n",
      "正在爬取第93页的数据\n",
      "正在爬取第94页的数据\n",
      "正在爬取第95页的数据\n",
      "正在爬取第96页的数据\n",
      "正在爬取第97页的数据\n",
      "正在爬取第98页的数据\n",
      "正在爬取第99页的数据\n",
      "终于爬完了累死了555555\n"
     ]
    }
   ],
   "source": [
    "# 爬取1-100页\n",
    "for i in range(1,100):\n",
    "    print(\"正在爬取第\" + str(i) + \"页的数据\")\n",
    "    url_pre = \"https://search.51job.com/list/030200,000000,0000,00,9,99,%25E6%2595%25B0%25E6%258D%25AE,2,\"\n",
    "    url_end = \".html?\"\n",
    "    url = url_pre + str(i) + url_end\n",
    "    \n",
    "    headers = {\n",
    "        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'\n",
    "    }\n",
    "    \n",
    "    web = requests.get(url, headers=headers)\n",
    "    web.encoding = \"gbk\"\n",
    "    \n",
    "    dom = etree.HTML(web.text)\n",
    "    \n",
    "    # XPATH上线\n",
    "    # 1、岗位名称\n",
    "    job_name = dom.xpath('//div[@class=\"dw_table\"]/div[@class=\"el\"]//p/span/a[@target=\"_blank\"]/@title')\n",
    "    # 2、公司名称\n",
    "    company_name = dom.xpath('//div[@class=\"dw_table\"]/div[@class=\"el\"]/span[@class=\"t2\"]/a[@target=\"_blank\"]/@title')\n",
    "    # 3、工作地点\n",
    "    address = dom.xpath('//div[@class=\"dw_table\"]/div[@class=\"el\"]/span[@class=\"t3\"]/text()')\n",
    "    # 4、工资\n",
    "    salary_mid = dom.xpath('//div[@class=\"dw_table\"]/div[@class=\"el\"]/span[@class=\"t4\"]')\n",
    "    salary = [i.text for i in salary_mid]\n",
    "    # 5、发布日期\n",
    "    release_time = dom.xpath('//div[@class=\"dw_table\"]/div[@class=\"el\"]/span[@class=\"t5\"]/text()')\n",
    "    # 6、获取二级网址url\n",
    "    deep_url = dom.xpath('//div[@class=\"dw_table\"]/div[@class=\"el\"]//p/span/a[@target=\"_blank\"]/@href')\n",
    "    \n",
    "    # 创建列表\n",
    "    RandomAll = []\n",
    "    JobDescribe = []\n",
    "    CompanyType = []\n",
    "    CompanySize = []\n",
    "    Industry = []\n",
    "    \n",
    "    # 进入详细页（6中取得的二级网址）爬取更多信息\n",
    "    for i in range(len(deep_url)):\n",
    "        web_test = requests.get(deep_url[i], headers=headers)\n",
    "        web_test.encoding = \"gbk\"\n",
    "        dom_test = etree.HTML(web_test.text)\n",
    "        \n",
    "        # 7、爬取经验、学历信息，先合在一个字段里面，以后再做数据清洗。命名为random_all\n",
    "        random_all = dom_test.xpath('//div[@class=\"tHeader tHjob\"]//div[@class=\"cn\"]/p[@class=\"msg ltype\"]/text()')\n",
    "        # 8、岗位描述性息\n",
    "        job_describe = dom_test.xpath('//div[@class=\"tBorderTop_box\"]//div[@class=\"bmsg job_msg inbox\"]/p/text()')\n",
    "        # 9、公司类型\n",
    "        company_type = dom_test.xpath('//div[@class=\"tCompany_sidebar\"]//div[@class=\"com_tag\"]/p[1]/@title')\n",
    "        # 10、公司规模(人数)\n",
    "        company_size = dom_test.xpath('//div[@class=\"tCompany_sidebar\"]//div[@class=\"com_tag\"]/p[2]/@title')\n",
    "        # 11、所属行业(公司)\n",
    "        industry = dom_test.xpath('//div[@class=\"tCompany_sidebar\"]//div[@class=\"com_tag\"]/p[3]/@title')\n",
    "        \n",
    "        # 将上述信息保存到各自的列表中\n",
    "        RandomAll.append(random_all)\n",
    "        JobDescribe.append(job_describe)\n",
    "        CompanyType.append(company_type)\n",
    "        CompanySize.append(company_size)\n",
    "        Industry.append(industry)\n",
    "        \n",
    "        # 为了反爬，设置睡眠时间\n",
    "        time.sleep(0.5)\n",
    "        \n",
    "    # 每页都要暂存，防止中途发生错误数据丢失\n",
    "    df = pd.DataFrame()\n",
    "    df[\"岗位名称\"] = job_name\n",
    "    df[\"公司名称\"] = company_name\n",
    "    df[\"工作地点\"] = address\n",
    "    df[\"工资\"] = salary\n",
    "    df[\"发布日期\"] = release_time\n",
    "    df[\"经验、学历\"] = RandomAll\n",
    "    df[\"公司类型\"] = CompanyType\n",
    "    df[\"公司规模\"] = CompanySize\n",
    "    df[\"所属行业\"] = Industry\n",
    "    df[\"岗位描述\"] = JobDescribe\n",
    "    \n",
    "    # 这里在写出过程中，有可能会写入失败，为了解决这个问题，我们使用异常处理。\n",
    "    try:\n",
    "        df.to_csv(\"job_info.csv\", mode=\"a+\", header=None, index=None, encoding=\"gbk\")\n",
    "    except:\n",
    "        print(\"当页数据写入失败\")\n",
    "    time.sleep(1)\n",
    "    \n",
    "print(\"终于爬完了累死了555555\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## 数据存储"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "# 数据处理\n",
    "\n",
    "df = pd.read_csv(r\"./job_info.csv\",engine=\"python\",header=None,encoding='gbk')\n",
    "# 为数据框指定行索引\n",
    "df.index = range(len(df))\n",
    "# 为数据框指定列索引\n",
    "df.columns = [\"岗位名\",\"公司名\",\"工作地点\",\"工资\",\"发布日期\",\"经验与学历\",\"公司类型\",\"公司规模\",\"行业\",\"工作描述\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hidden": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>岗位名</th>\n",
       "      <th>公司名</th>\n",
       "      <th>工作地点</th>\n",
       "      <th>工资</th>\n",
       "      <th>发布日期</th>\n",
       "      <th>经验与学历</th>\n",
       "      <th>公司类型</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>行业</th>\n",
       "      <th>工作描述</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据库工程师</td>\n",
       "      <td>综合平台开发运营中心</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-15</td>\n",
       "      <td>['广州\\xa0\\xa0', '\\xa0\\xa0硕士\\xa0\\xa0', '\\xa0\\xa0...</td>\n",
       "      <td>['国企']</td>\n",
       "      <td>['']</td>\n",
       "      <td>['通信/电信运营、增值服务']</td>\n",
       "      <td>['1、负责数据库的日常施工支撑和性能优化工作；', '2、负责公司产品线的大型分布式数据库...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>大数据开发</td>\n",
       "      <td>联通（广东）产业互联网有限公司</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-15</td>\n",
       "      <td>['广州\\xa0\\xa0', '\\xa0\\xa0本科\\xa0\\xa0', '\\xa0\\xa0...</td>\n",
       "      <td>['国企']</td>\n",
       "      <td>['']</td>\n",
       "      <td>['通信/电信运营、增值服务']</td>\n",
       "      <td>['职位描述：', '以下职位方向可选其一：', '1.负责大数据平台及产品的研发工作；',...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>23元/时  数据线质检  无需经验</td>\n",
       "      <td>广州遂昌电子科技有限公司</td>\n",
       "      <td>广州-黄埔区</td>\n",
       "      <td>6-8千/月</td>\n",
       "      <td>07-15</td>\n",
       "      <td>['广州-黄埔区\\xa0\\xa0', '\\xa0\\xa0无需经验\\xa0\\xa0', '\\x...</td>\n",
       "      <td>['民营公司']</td>\n",
       "      <td>['500-1000人']</td>\n",
       "      <td>['电子技术/半导体/集成电路,通信/电信/网络设备']</td>\n",
       "      <td>['本公司主要生产较新上市的手机和电脑配件', '穿普通工衣上班，做事轻松，不是流水线，全部...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>数据线质检员 22元一小时</td>\n",
       "      <td>广州遂昌电子科技有限公司</td>\n",
       "      <td>广州-黄埔区</td>\n",
       "      <td>6-8千/月</td>\n",
       "      <td>07-15</td>\n",
       "      <td>['广州-黄埔区\\xa0\\xa0', '\\xa0\\xa0无需经验\\xa0\\xa0', '\\x...</td>\n",
       "      <td>['民营公司']</td>\n",
       "      <td>['500-1000人']</td>\n",
       "      <td>['电子技术/半导体/集成电路,机械/设备/重工']</td>\n",
       "      <td>['无需面试，暑假工，社会工都可以做', '直接到厂面试！！！', '因新开产线，需要大量补...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>高级数据分析师</td>\n",
       "      <td>唯品会（中国）有限公司</td>\n",
       "      <td>广州-荔湾区</td>\n",
       "      <td>1.5-3万/月</td>\n",
       "      <td>07-15</td>\n",
       "      <td>['广州-荔湾区\\xa0\\xa0', '\\xa0\\xa05-7年经验\\xa0\\xa0', '...</td>\n",
       "      <td>['上市公司']</td>\n",
       "      <td>['10000人以上']</td>\n",
       "      <td>['互联网/电子商务']</td>\n",
       "      <td>['职位描述:', '1、\\xa0配合业务发展与规划，对各部门进行数据需求支持，挖掘分析主题...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  岗位名              公司名    工作地点        工资   发布日期  \\\n",
       "0              数据库工程师       综合平台开发运营中心      广州       NaN  07-15   \n",
       "1               大数据开发  联通（广东）产业互联网有限公司      广州       NaN  07-15   \n",
       "2  23元/时  数据线质检  无需经验     广州遂昌电子科技有限公司  广州-黄埔区    6-8千/月  07-15   \n",
       "3       数据线质检员 22元一小时     广州遂昌电子科技有限公司  广州-黄埔区    6-8千/月  07-15   \n",
       "4             高级数据分析师      唯品会（中国）有限公司  广州-荔湾区  1.5-3万/月  07-15   \n",
       "\n",
       "                                               经验与学历      公司类型           公司规模  \\\n",
       "0  ['广州\\xa0\\xa0', '\\xa0\\xa0硕士\\xa0\\xa0', '\\xa0\\xa0...    ['国企']           ['']   \n",
       "1  ['广州\\xa0\\xa0', '\\xa0\\xa0本科\\xa0\\xa0', '\\xa0\\xa0...    ['国企']           ['']   \n",
       "2  ['广州-黄埔区\\xa0\\xa0', '\\xa0\\xa0无需经验\\xa0\\xa0', '\\x...  ['民营公司']  ['500-1000人']   \n",
       "3  ['广州-黄埔区\\xa0\\xa0', '\\xa0\\xa0无需经验\\xa0\\xa0', '\\x...  ['民营公司']  ['500-1000人']   \n",
       "4  ['广州-荔湾区\\xa0\\xa0', '\\xa0\\xa05-7年经验\\xa0\\xa0', '...  ['上市公司']   ['10000人以上']   \n",
       "\n",
       "                             行业  \\\n",
       "0              ['通信/电信运营、增值服务']   \n",
       "1              ['通信/电信运营、增值服务']   \n",
       "2  ['电子技术/半导体/集成电路,通信/电信/网络设备']   \n",
       "3    ['电子技术/半导体/集成电路,机械/设备/重工']   \n",
       "4                  ['互联网/电子商务']   \n",
       "\n",
       "                                                工作描述  \n",
       "0  ['1、负责数据库的日常施工支撑和性能优化工作；', '2、负责公司产品线的大型分布式数据库...  \n",
       "1  ['职位描述：', '以下职位方向可选其一：', '1.负责大数据平台及产品的研发工作；',...  \n",
       "2  ['本公司主要生产较新上市的手机和电脑配件', '穿普通工衣上班，做事轻松，不是流水线，全部...  \n",
       "3  ['无需面试，暑假工，社会工都可以做', '直接到厂面试！！！', '因新开产线，需要大量补...  \n",
       "4  ['职位描述:', '1、\\xa0配合业务发展与规划，对各部门进行数据需求支持，挖掘分析主题...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 让我看看\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "# 数据处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true,
    "hidden": true
   },
   "source": [
    "## 岗位名处理（去重）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "去重之前的记录数 (5849, 10)\n",
      "去重之后的记录数 (4638, 10)\n"
     ]
    }
   ],
   "source": [
    "# 去重之前的记录数\n",
    "print(\"去重之前的记录数\",df.shape)\n",
    "# 记录去重\n",
    "df.drop_duplicates(subset=[\"公司名\",\"岗位名\"],inplace=True)\n",
    "# 去重之后的记录数\n",
    "print(\"去重之后的记录数\",df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "数据分析师            92\n",
       "大数据开发工程师         51\n",
       "数据专员             44\n",
       "仓库管理员            38\n",
       "数据文员             28\n",
       "                 ..\n",
       "Python数据开发工程师     1\n",
       "主播经纪人/主播运营        1\n",
       "新媒体文案运营主管         1\n",
       "电商采购              1\n",
       "高级产品管理开发专员        1\n",
       "Name: 岗位名, Length: 3217, dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 岗位名筛选（还有带英文名的进行统一）\n",
    "\n",
    "display(df[\"岗位名\"].value_counts())\n",
    "\n",
    "df[\"岗位名\"] = df[\"岗位名\"].apply(lambda x:x.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2509, 10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 岗位筛选分析\n",
    "import numpy as np\n",
    "\n",
    "# job_info.shape\n",
    "\n",
    "target_job = ['算法', '开发', '分析', '工程师', '数据', '运营', '运维','产品']\n",
    "index = [df[\"岗位名\"].str.count(i) for i in target_job]\n",
    "index = np.array(index).sum(axis=0) > 0\n",
    "job_info = df[index]\n",
    "job_info.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "job_list = ['数据分析', \"数据统计\",\"数据专员\",'数据挖掘', '算法',\n",
    "            '大数据','开发工程师', '运营', '软件工程', '前端开发',\n",
    "            '深度学习', 'ai', '数据库', '数据库', '数据产品',\n",
    "            '客服', 'java', '.net', 'andrio', '人工智能', 'c++',\n",
    "            '数据管理',\"测试\",\"运维\"]\n",
    "job_list = np.array(job_list)\n",
    "def rename(x=None,job_list=job_list):\n",
    "    index = [i in x for i in job_list]\n",
    "    if sum(index) > 0:\n",
    "        return job_list[index][0]\n",
    "    else:\n",
    "        return x\n",
    "job_info[\"岗位名\"] = job_info[\"岗位名\"].apply(rename)\n",
    "job_info[\"岗位名\"].value_counts()\n",
    "# 数据统计、数据专员、数据分析统一归为数据分析\n",
    "job_info[\"岗位名\"] = job_info[\"岗位名\"].apply(lambda x:re.sub(\"数据专员\",\"数据分析\",x))\n",
    "job_info[\"岗位名\"] = job_info[\"岗位名\"].apply(lambda x:re.sub(\"数据统计\",\"数据分析\",x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 工资处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "job_info[\"工资\"].str[-1].value_counts()\n",
    "job_info[\"工资\"].str[-3].value_counts()\n",
    "\n",
    "index1 = job_info[\"工资\"].str[-1].isin([\"年\",\"月\"])\n",
    "index2 = job_info[\"工资\"].str[-3].isin([\"万\",\"千\"])\n",
    "job_info = job_info[index1 & index2]\n",
    "\n",
    "def get_money_max_min(x):\n",
    "    try:\n",
    "        if x[-3] == \"万\":\n",
    "            z = [float(i)*10000 for i in re.findall(\"[0-9]+\\.?[0-9]*\",x)]\n",
    "        elif x[-3] == \"千\":\n",
    "            z = [float(i) * 1000 for i in re.findall(\"[0-9]+\\.?[0-9]*\", x)]\n",
    "        if x[-1] == \"年\":\n",
    "            z = [i/12 for i in z]\n",
    "        return z\n",
    "    except:\n",
    "        return x\n",
    "\n",
    "salary = job_info[\"工资\"].apply(get_money_max_min)\n",
    "job_info[\"最低工资\"] = salary.str[0]\n",
    "job_info[\"最高工资\"] = salary.str[1]\n",
    "job_info[\"工资水平\"] = job_info[[\"最低工资\",\"最高工资\"]].mean(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 公司类型处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "job_info.loc[job_info[\"公司类型\"].apply(lambda x:len(x)<6),\"公司类型\"] = np.nan\n",
    "job_info[\"公司类型\"] = job_info[\"公司类型\"].str[2:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 行业处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "job_info[\"行业\"] = job_info[\"行业\"].apply(lambda x:re.sub(\",\",\"/\",x))\n",
    "job_info.loc[job_info[\"行业\"].apply(lambda x:len(x)<6),\"行业\"] = np.nan\n",
    "job_info[\"行业\"] = job_info[\"行业\"].str[2:-2].str.split(\"/\").str[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 学历经验处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "job_info[\"学历\"] = job_info[\"经验与学历\"].apply(lambda x:re.findall(\"本科|大专|应届生|在校生|硕士\",x))\n",
    "def func(x):\n",
    "    if len(x) == 0:\n",
    "        return np.nan\n",
    "    elif len(x) == 1 or len(x) == 2:\n",
    "        return x[0]\n",
    "    else:\n",
    "        return x[2]\n",
    "job_info[\"学历\"] = job_info[\"学历\"].apply(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "##  公司规模处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "def func(x):\n",
    "    if x == \"['少于50人']\":\n",
    "        return \"<50\"\n",
    "    elif x == \"['50-150人']\":\n",
    "        return \"50-150\"\n",
    "    elif x == \"['150-500人']\":\n",
    "        return '150-500'\n",
    "    elif x == \"['500-1000人']\":\n",
    "        return '500-1000'\n",
    "    elif x == \"['1000-5000人']\":\n",
    "        return '1000-5000'\n",
    "    elif x == \"['5000-10000人']\":\n",
    "        return '5000-10000'\n",
    "    elif x == \"['10000人以上']\":\n",
    "        return \">10000\"\n",
    "    else:\n",
    "        return np.nan\n",
    "job_info[\"公司规模\"] = job_info[\"公司规模\"].apply(func)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "## 清洗后存储数据为新表"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "hidden": true
   },
   "outputs": [],
   "source": [
    "列名 = [\"公司名\",\"岗位名\",\"工作地点\",\"工资水平\",\"发布日期\",\"学历\",\"公司类型\",\"公司规模\",\"行业\",\"工作描述\"]\n",
    "df_清洗后 = job_info[列名]\n",
    "df_清洗后.fillna(value='无')\n",
    "df_清洗后.to_excel(r\"./清洗后job_info.xlsx\",encoding=\"gbk\",index=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 数据分析\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 读取\n",
    "df_清洗后=pd.read_excel('./清洗后job_info.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>公司名</th>\n",
       "      <th>岗位名</th>\n",
       "      <th>工作地点</th>\n",
       "      <th>工资水平</th>\n",
       "      <th>发布日期</th>\n",
       "      <th>学历</th>\n",
       "      <th>公司类型</th>\n",
       "      <th>公司规模</th>\n",
       "      <th>行业</th>\n",
       "      <th>工作描述</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2465</td>\n",
       "      <td>2465</td>\n",
       "      <td>2465</td>\n",
       "      <td>2465.000000</td>\n",
       "      <td>2465</td>\n",
       "      <td>2198</td>\n",
       "      <td>2460</td>\n",
       "      <td>2447</td>\n",
       "      <td>2461</td>\n",
       "      <td>2465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>2414</td>\n",
       "      <td>512</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>56</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>56</td>\n",
       "      <td>2256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>广东鑫盛弘创资产管理有限公司</td>\n",
       "      <td>运营</td>\n",
       "      <td>广州</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07-15</td>\n",
       "      <td>大专</td>\n",
       "      <td>民营公司</td>\n",
       "      <td>50-150</td>\n",
       "      <td>互联网</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>10</td>\n",
       "      <td>708</td>\n",
       "      <td>2368</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1311</td>\n",
       "      <td>1223</td>\n",
       "      <td>1923</td>\n",
       "      <td>778</td>\n",
       "      <td>475</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10094.235970</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11457.782742</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1750.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   公司名   岗位名  工作地点           工资水平   发布日期    学历  公司类型    公司规模  \\\n",
       "count             2465  2465  2465    2465.000000   2465  2198  2460    2447   \n",
       "unique            2414   512     2            NaN     56     4    10       7   \n",
       "top     广东鑫盛弘创资产管理有限公司    运营    广州            NaN  07-15    大专  民营公司  50-150   \n",
       "freq                10   708  2368            NaN   1311  1223  1923     778   \n",
       "mean               NaN   NaN   NaN   10094.235970    NaN   NaN   NaN     NaN   \n",
       "std                NaN   NaN   NaN   11457.782742    NaN   NaN   NaN     NaN   \n",
       "min                NaN   NaN   NaN    1750.000000    NaN   NaN   NaN     NaN   \n",
       "25%                NaN   NaN   NaN    6000.000000    NaN   NaN   NaN     NaN   \n",
       "50%                NaN   NaN   NaN    8000.000000    NaN   NaN   NaN     NaN   \n",
       "75%                NaN   NaN   NaN   12500.000000    NaN   NaN   NaN     NaN   \n",
       "max                NaN   NaN   NaN  500000.000000    NaN   NaN   NaN     NaN   \n",
       "\n",
       "          行业  工作描述  \n",
       "count   2461  2465  \n",
       "unique    56  2256  \n",
       "top      互联网    []  \n",
       "freq     475   190  \n",
       "mean     NaN   NaN  \n",
       "std      NaN   NaN  \n",
       "min      NaN   NaN  \n",
       "25%      NaN   NaN  \n",
       "50%      NaN   NaN  \n",
       "75%      NaN   NaN  \n",
       "max      NaN   NaN  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看\n",
    "df_清洗后.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业需求统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [],
   "source": [
    "行业=df_清洗后.行业\n",
    "# 行业"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "行业\n",
       "互联网                  475\n",
       "计算机软件                400\n",
       "快速消费品(食品、饮料、化妆品)     157\n",
       "贸易                   140\n",
       "服装                   135\n",
       "计算机服务(系统、数据服务、维修)     99\n",
       "通信                    93\n",
       "专业服务(咨询、人力资源、财会)      68\n",
       "电子技术                  67\n",
       "批发                    59\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "行业数统计=df_清洗后.groupby(by='行业').size().sort_values(ascending=False).head(10)\n",
    "display(行业数统计)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 中文\n",
    "# import matplotlib.pyplot as plt\n",
    "# plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "# plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 中文怎么还不行烦死了烦死了烦死了\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['Arial Unicode MS']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1528827f0>"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "行业数统计.plot(figsize=(10,10),fontsize='14',kind='bar',title='广州市数据相关各行业需求',grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 岗位数量统计"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "岗位=df_清洗后.岗位名"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "岗位名\n",
       "运营       708\n",
       "数据分析     557\n",
       "大数据      187\n",
       "开发工程师    164\n",
       "数据库       66\n",
       "运维        32\n",
       "数据管理      29\n",
       "数据文员      28\n",
       "测试        25\n",
       "数据产品      17\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "岗位统计=df_清洗后.groupby(by='岗位名').size().sort_values(ascending=False).head(10)\n",
    "display(岗位统计)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x152840240>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "岗位统计.plot(figsize=(14,7),\\\n",
    "             kind='bar',\\\n",
    "             color='yellowgreen',\\\n",
    "             title='广州市数据相关各岗位数量',\\\n",
    "             fontsize='17',\\\n",
    "             grid=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 岗位薪资水平（取平均值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 岗位薪资水平（取平均）\n",
    "岗位薪资=df_清洗后.groupby(by='岗位名').agg({\"工资水平\":[\"mean\"]}).sort_values(by=(\"工资水平\",\"mean\"),ascending=False).reset_index().head(10)#.iloc[7:17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>岗位名</th>\n",
       "      <th>工资水平</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>数据中心总经理</td>\n",
       "      <td>500000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>数据中心营运总监</td>\n",
       "      <td>45833.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>数据总监</td>\n",
       "      <td>37500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>博士后研究员（数据传输与交换）</td>\n",
       "      <td>36666.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>群益科技--数据中心经理（社会招聘）</td>\n",
       "      <td>35000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>高级web 全栈开发</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>数据模型工程师：</td>\n",
       "      <td>30000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>销售工程师（弹性时间、各项福利、底薪+奖金）</td>\n",
       "      <td>29166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>数据架构师（数据中台方向）</td>\n",
       "      <td>27500.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>数据技术研究员</td>\n",
       "      <td>27500.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      岗位名           工资水平\n",
       "                                    mean\n",
       "0                 数据中心总经理  500000.000000\n",
       "1                数据中心营运总监   45833.333333\n",
       "2                    数据总监   37500.000000\n",
       "3         博士后研究员（数据传输与交换）   36666.666667\n",
       "4      群益科技--数据中心经理（社会招聘）   35000.000000\n",
       "5              高级web 全栈开发   30000.000000\n",
       "6                数据模型工程师：   30000.000000\n",
       "7  销售工程师（弹性时间、各项福利、底薪+奖金）   29166.666667\n",
       "8           数据架构师（数据中台方向）   27500.000000\n",
       "9                 数据技术研究员   27500.000000"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看平均薪资\n",
    "岗位薪资"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['数据中心总经理',\n",
       " '数据中心营运总监',\n",
       " '数据总监',\n",
       " '博士后研究员',\n",
       " '群益科技--数据中心经理',\n",
       " '高级web 全栈开发',\n",
       " '数据模型工程师：',\n",
       " '销售工程师',\n",
       " '数据架构师',\n",
       " '数据技术研究员']"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 处理下岗位名‘（）’里的不要🙅\n",
    "a=岗位薪资['岗位名'].to_list()\n",
    "b=[]\n",
    "for i in a:\n",
    "    b.append(str(i).split('（'))\n",
    "# print(b)\n",
    "岗位名_处理=[]\n",
    "for x in b:\n",
    "    岗位名_处理.append(x[0])\n",
    "岗位名_处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画图\n",
    "bar = plt.bar(岗位名_处理,岗位薪资[\"工资水平\",\"mean\"].values, color='dodgerblue')\n",
    "bar[0].set_color('green')\n",
    "\n",
    "# 给条形图添加数据标注\n",
    "for x, y in enumerate(岗位薪资[\"工资水平\",\"mean\"].values):\n",
    "    plt.text(x-0.4, y+500, \"%s\" %y)\n",
    "\n",
    "plt.tick_params(labelsize=10)\n",
    "plt.xlabel('岗位名')\n",
    "plt.ylabel([\"工资水平\",\"mean\"])\n",
    "plt.title('各岗位薪资平均水平')\n",
    "plt.rcParams['figure.figsize'] = (14, 7)\n",
    "# plt.rcParams['savefig.dpi'] = 100 #图片像素\n",
    "# plt.rcParams['figure.dpi'] = 100 #分辨率"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 行业薪资水平（平均值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th colspan=\"3\" halign=\"left\">工资水平</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>最小值</th>\n",
       "      <th>最大值</th>\n",
       "      <th>平均值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>房地产</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>22113.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>租赁服务</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>14166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石油</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>13552.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中介服务</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>12600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>网络游戏</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>12183.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>计算机软件</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>41666.666667</td>\n",
       "      <td>12129.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>多元化业务集团公司</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>45833.333333</td>\n",
       "      <td>11731.481481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>学术</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>23333.333333</td>\n",
       "      <td>11541.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>电气</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>20833.333333</td>\n",
       "      <td>11238.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>教育</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>11155.660377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          行业    工资水平                             \n",
       "                 最小值            最大值           平均值\n",
       "0        房地产  2500.0  500000.000000  22113.703704\n",
       "1       租赁服务  5000.0   25000.000000  14166.666667\n",
       "2         石油  7500.0   20000.000000  13552.083333\n",
       "3       中介服务  7000.0   25000.000000  12600.000000\n",
       "4       网络游戏  3700.0   27500.000000  12183.333333\n",
       "5      计算机软件  2500.0   41666.666667  12129.583333\n",
       "6  多元化业务集团公司  3750.0   45833.333333  11731.481481\n",
       "7         学术  5000.0   23333.333333  11541.666667\n",
       "8         电气  5250.0   20833.333333  11238.888889\n",
       "9         教育  2500.0   35000.000000  11155.660377"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "行业工资水平_前10=df_清洗后.groupby(by='行业').agg({\"工资水平\":[\"min\",\"max\",\"mean\"]}).rename(columns={'min':'最小值','max':'最大值','mean':'平均值'}).sort_values(by=(\"工资水平\",\"平均值\"),ascending=False).head(10).reset_index()\n",
    "行业工资水平_前10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>行业</th>\n",
       "      <th colspan=\"3\" halign=\"left\">工资水平</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>最小值</th>\n",
       "      <th>最大值</th>\n",
       "      <th>平均值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>房地产</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>500000.000000</td>\n",
       "      <td>22113.703704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>租赁服务</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>14166.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>石油</td>\n",
       "      <td>7500.0</td>\n",
       "      <td>20000.000000</td>\n",
       "      <td>13552.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>中介服务</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>12600.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>网络游戏</td>\n",
       "      <td>3700.0</td>\n",
       "      <td>27500.000000</td>\n",
       "      <td>12183.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>计算机软件</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>41666.666667</td>\n",
       "      <td>12129.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>多元化业务集团公司</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>45833.333333</td>\n",
       "      <td>11731.481481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>学术</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>23333.333333</td>\n",
       "      <td>11541.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>电气</td>\n",
       "      <td>5250.0</td>\n",
       "      <td>20833.333333</td>\n",
       "      <td>11238.888889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>教育</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>35000.000000</td>\n",
       "      <td>11155.660377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          行业    工资水平                             \n",
       "                 最小值            最大值           平均值\n",
       "0        房地产  2500.0  500000.000000  22113.703704\n",
       "1       租赁服务  5000.0   25000.000000  14166.666667\n",
       "2         石油  7500.0   20000.000000  13552.083333\n",
       "3       中介服务  7000.0   25000.000000  12600.000000\n",
       "4       网络游戏  3700.0   27500.000000  12183.333333\n",
       "5      计算机软件  2500.0   41666.666667  12129.583333\n",
       "6  多元化业务集团公司  3750.0   45833.333333  11731.481481\n",
       "7         学术  5000.0   23333.333333  11541.666667\n",
       "8         电气  5250.0   20833.333333  11238.888889\n",
       "9         教育  2500.0   35000.000000  11155.660377"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 行业薪资\n",
    "行业工资水平_前10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 行业工资水平画图\n",
    "bar = plt.bar(行业工资水平_前10['行业'],行业工资水平_前10[\"工资水平\",\"平均值\"].values, color='dodgerblue')\n",
    "bar[0].set_color('green')\n",
    "\n",
    "# 给条形图添加数据标注\n",
    "for x, y in enumerate(行业工资水平_前10[\"工资水平\",\"平均值\"].values):\n",
    "    plt.text(x-0.4, y+500, \"%s\" %y)\n",
    "\n",
    "plt.tick_params(labelsize=10)\n",
    "plt.xlabel('行业名')\n",
    "plt.ylabel([\"工资水平\",\"平均值\"])\n",
    "plt.title('各行业薪资平均水平')\n",
    "plt.rcParams['figure.figsize'] = (14, 7)\n",
    "# plt.rcParams['savefig.dpi'] = 100 #图片像素\n",
    "# plt.rcParams['figure.dpi'] = 100 #分辨率"
   ]
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